Medical Imaging

Seminar: Computational Medical Imaging (CoMI)

The Computational Imaging Research (CoMI) Seminar at the University of Bonn explores current research in Computational Medical Imaging, including classical algorithms and deep learning-based approaches. Students critically analyze state-of-the-art research and develop both technical and presentation skills.

Special Version: AI in Radiology – From Pixels to Decisions (AiR-GUC Edition)

A special joint seminar between the University of Bonn and the German University in Cairo (GUC), delivered under the Joint Teaching Initiative. This interdisciplinary course explores AI in Radiology from both technical and clinical perspectives, combining computational medical imaging and deep learning to foster cross-cultural collaboration, innovation, and sustainable academic exchange.

CIR/01/2025: Postdoc/Research Staff position in Computational Medical Imaging (m/f/d)

starting Oct. 2025 or as agreed upon. The position is initially limited to one year.

Summer School on Biomedical Imaging with Deep Learning (BILD)

DAAD Funded Project (2025-2025)

Strategic Arab-German Network for Affordable and Democratized AI (SANAD)

DAAD Funded Project (2025-2025)

The lab contributes to the Organizing and Program Committees of MICCAI'25

Our lab is proud to contribute to the organization of MICCAI'25. Prof. Dr. Shadi Albarqouni serves as a member of the Outreach Committee, and Dr. Elodie Germani will serve an Area Chair at MICCAI'25

BA/MA thesis on Modeling brain changes related to physical activity with machine learning

Abstract. In the last decade, several studies suggested that physical fitness may positively influence brain and cardiovascular health. Brain health is usually assessed through structural and functional imaging techniques to extract biomarkers of aging that can be used to predict brain age ( Dunås et al.

Application of AI and Advanced MRI for a Better Prediction of Disease Progression in Neurological Disorders

Bonn International Fellowships (2024 - 2025)

MA Thesis: Deep Learning for Lymph Node Metastasis Detection in Pancreatic Ductal Adenocarcinoma (Not available)

Abstract: Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers, with lymph node metastasis (LNM) being a critical determinant in patient prognosis and therapeutic planning [1-2]. Conventional methods for detecting LNM in PDAC primarily rely on contrast-enhanced CT scans, but these often fall short in sensitivity, especially in early-stage disease.

MA Thesis: Development of a Machine Learning Algorithm for Histopathological Classification of Conjunctival Melanocytic Intraepithelial Lesions -- Not available

Abstract. Conjunctival Melanocytic Intraepithelial Lesions (CMIL) are a significant precursor to conjunctival melanoma, a rare but potentially fatal ocular cancer. The histopathological classification of CMIL is crucial for early diagnosis and treatment planning.